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A Non-parametric Method for Defining a Global Preference Ranking of Industrial Products

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  • L. Corain
  • L. Salmaso

Abstract

Although experimentation is a crucial stage in the process of research and development of industrial products, no satisfactory procedure is available to deal with the common but rather important industrial problem of defining a preference ranking among all the studied product prototypes on the basis of performances. In this paper we propose a two-stage non-parametric procedure in which we firstly perform a set of C-sample testing procedures, followed by multiple comparisons, in this way evaluating a set of partial preference rankings, and secondly synthesise the partial rankings by combining them into a global ranking that provides a general product preference rule. The proposed method is particularly useful in the context of industrial experimentation and offers several advantages such as effectiveness, high flexibility and practical adherence to real problems where preference ranking is a natural goal.

Suggested Citation

  • L. Corain & L. Salmaso, 2007. "A Non-parametric Method for Defining a Global Preference Ranking of Industrial Products," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(2), pages 203-216.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:203-216
    DOI: 10.1080/02664760600995122
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    References listed on IDEAS

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    1. Luigi Salmaso & Aldo Solari, 2005. "Multiple aspect testing for case-control designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 62(2), pages 331-340, November.
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    Cited by:

    1. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.

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